Inference on a Stochastic Bass Model for New Product Adoption

Abstract

This paper investigates stochastic versions of the classical Bass diffusion model, with a focus on the development and comparison of parameter estimation procedures. Two stochastic processes that generalize the classical Bass model, which is commonly used to describe sales dynamics in emerging markets, are considered. For these processes, parameter estimation procedures that identify the potential market size, the innovation and imitation rates, and the amplitude of random fluctuations of the phenomenon under study are developed. The first estimation procedure is based on a discretization of the original process and employs a quasi-maximum likelihood approach. The second procedure relies on a new parametrization of the Bass curve and leads to a maximum likelihood estimation method. An extensive simulation study is presented to assess the statistical accuracy and robustness of the proposed procedures and to provide a comparative evaluation of their performance under different scenarios. Finally, the applicability of the proposed inferential framework is illustrated through an empirical study on mobile social networking adoption.